This introductory chapter will get you started with the basics of R which include various constructs, useful data structures, loops and vectorization. If you are already an R wizard, you can skim through these sections and dive right into the next part which talks about what machine learning actually represents as a domain and the main areas it encompasses. We will also talk about different machine learning techniques and algorithms used in each area. Finally, we will conclude by looking at some of the most popular machine learning packages in R, some of which we will be using in the subsequent chapters.
If you are a data or machine learning enthusiast, surely you would have heard by now that being a data scientist is referred to as the sexiest job of the 21st century by Harvard Business Review.
There is a huge demand in the current market for data scientists, primarily because their main job is to gather crucial insights and information from both unstructured and structured data to help their business and organization grow strategically.
Some of you might be wondering how machine learning or R relate to all this! Well, to be a successful data scientist, one of the major tools you need in your toolbox is a powerful language capable of performing complex statistical calculations and working with various types of data and building models which help you get previously unknown insights and R is the perfect language for that! Machine learning forms the foundation of the skills you need to build to become a data analyst or data scientist, this includes using various techniques to build models to get insights from data.
This book will provide you with some of the essential tools you need to be well versed with both R and machine learning by not only looking at concepts but also applying those concepts in real-world examples. Enough talk; now let's get started on our journey into the world of machine learning with R!
In this chapter, we will cover the following aspects:
- Delving into the basics of R
- Understanding the data structures in R
- Working with functions
- Controlling code flow
- Taking further steps with R
- Understanding machine learning basics
- Familiarizing yourself with popular machine learning packages in R
It is assumed here that you are at least familiar with the basics of R or have worked with R before. Hence, we won't be talking much about downloading and installations. There are plenty of resources on the web which provide a lot of information on this. I recommend that you use RStudio which is an Integrated Development Environment (IDE), which is much better than the base R Graphical User Interface (GUI). You can visit https://www.rstudio.com/ to get more information about it.
Note
For details about the R project, you can visit https://www.r-project.org/ to get an overview of the language. Besides this, R has a vast arsenal of wonderful packages at its disposal and you can view everything related to R and its packages at https://cran.r-project.org/ which contains all the archives.
You must already be familiar with the R interactive interpreter, often called a Read-Evaluate-Print
Loop (REPL). This interpreter acts like any command line interface which asks for input and starts with a > character, which indicates that R is waiting for your input. If your input spans multiple lines, like when you are writing a function, you will see a + prompt in each subsequent line, which means that you didn't finish typing the complete expression and R is asking you to provide the rest of the expression.
It is also possible for R to read and execute complete files containing commands and functions which are saved in files with an .R extension. Usually, any big application consists of several .R files. Each file has its own role in the application and is often called as a module. We will be exploring some of the main features and capabilities of R in the following sections.
The most basic constructs in R include variables and arithmetic operators which can be used to perform simple mathematical operations like a calculator or even complex statistical calculations.
> 5 + 6 [1] 11 > 3 * 2 [1] 6 > 1 / 0 [1] Inf
Remember that everything in R is a vector. Even the output results indicated in the previous code snippet. They have a leading [1] symbol indicating it is a vector of size 1.
You can also assign values to variables and operate on them just like any other programming language.
> num <- 6 > num ^ 2 [1] 36 > num [1] 6 # a variable changes value only on re-assignment > num <- num ^ 2 * 5 + 10 / 3 > num [1] 183.3333
The most basic data structure in R is a vector. Basically, anything in R is a vector, even if it is a single number just like we saw in the earlier example! A vector is basically a sequence or a set of values. We can create vectors using the : operator or the c function which concatenates the values to create a vector.
> x <- 1:5 > x [1] 1 2 3 4 5 > y <- c(6, 7, 8 ,9, 10) > y [1] 6 7 8 9 10 > z <- x + y > z [1] 7 9 11 13 15
You can clearly in the previous code snippet, that we just added two vectors together without using any loop, using just the + operator. This is known as vectorization and we will be discussing more about this later on. Some more operations on vectors are shown next:
> c(1,3,5,7,9) * 2 [1] 2 6 10 14 18 > c(1,3,5,7,9) * c(2, 4) [1] 2 12 10 28 18 # here the second vector gets recycled
Output:
> factorial(1:5) [1] 1 2 6 24 120 > exp(2:10) # exponential function [1] 7.389056 20.085537 54.598150 148.413159 403.428793 1096.633158 [7] 2980.957987 8103.083928 22026.465795 > cos(c(0, pi/4)) # cosine function [1] 1.0000000 0.7071068 > sqrt(c(1, 4, 9, 16)) [1] 1 2 3 4 > sum(1:10) [1] 55
You might be confused with the second operation where we tried to multiply a smaller vector with a bigger vector but we still got a result! If you look closely, R threw a warning also. What happened in this case is, since the two vectors were not equal in size, the smaller vector in this case c(2, 4) got recycled or repeated to become c(2, 4, 2, 4, 2) and then it got multiplied with the first vector c(1, 3, 5, 7 ,9) to give the final result vector, c(2, 12, 10, 28, 18). The other functions mentioned here are standard functions available in base R along with several other functions.
Tip
Downloading the example code
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- Enter the name of the book in the Search box
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Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:
- WinRAR / 7-Zip for Windows
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Since you will be dealing with a lot of messy and dirty data in data analysis and machine learning, it is important to remember some of the special values in R so that you don't get too surprised later on if one of them pops up.
> 1 / 0 [1] Inf > 0 / 0 [1] NaN > Inf / NaN [1] NaN > Inf / Inf [1] NaN > log(Inf) [1] Inf > Inf + NA [1] NA
The main values which should concern you here are Inf which stands for Infinity, NaN which is Not a Number, and NA which indicates a value that is missing or Not Available. The following code snippet shows some logical tests on these special values and their results. Do remember that TRUE and FALSE are logical data type values, similar to other programming languages.
> vec <- c(0, Inf, NaN, NA) > is.finite(vec) [1] TRUE FALSE FALSE FALSE > is.nan(vec) [1] FALSE FALSE TRUE FALSE > is.na(vec) [1] FALSE FALSE TRUE TRUE > is.infinite(vec) [1] FALSE TRUE FALSE FALSE
The functions are pretty self-explanatory from their names. They clearly indicate which values are finite, which are finite and checks for NaN and NA values respectively. Some of these functions are very useful when cleaning dirty data.